Epidemic tracking and forecasting: Lessons learned from a tumultuous year

Abstract

Epidemic forecasting has garnered increasing interest in the last decade, nurtured and scaffolded by various forecasting challenges organized by groups within the US federal government, including the Centers for Disease Control and Prevention (CDC) (1⇓–3), Office of Science and Technology Policy (OSTP) (4), and Defense Advanced Research Projects Agency (DARPA) (5), and elsewhere (6, 7). In 2017, after several years of experimentation with flu forecasting in academic groups, the CDC decided to incorporate influenza forecasting into its normal operations, including weekly public communications (8) and briefing to higher-ups. To provide more reliable infrastructure and support for its forecasting needs, the CDC in 2019 designated two national Centers of Excellence for Influenza Forecasting, one at the University of Massachusetts at Amherst (https://reichlab.io/people) and one at Carnegie Mellon University (https://delphi.cmu.edu/about/center-of-excellence/). Not unrelatedly, the last decade has also seen a rise in the importance of digital surveillance streams in public health, with improving epidemic tracking and forecasting models being a key application of these data. Digital streams, such as search and social media trends, have constituted a large part of the focus (9⇓⇓⇓⇓–14); however, even more broadly, data from auxiliary streams that operate outside of traditional public health reporting, such as online surveys, medical devices, or electronic medical records (EMRs), have received considerable attention as well (15⇓⇓⇓⇓⇓⇓⇓⇓⇓–25). The Carnegie Mellon Delphi group, which the two of us colead, has worked in both of these emerging disciplines—epidemic forecasting and building relevant auxiliary signals to aid such forecasting models—since 2012. In 2020, as the pandemic broke out, we struggled like many other groups to find ways to contribute to the national efforts to respond to the pandemic. We ended up shifting our … [↵][1]1To whom correspondence may be addressed. Email: ryantibs{at}cmu.edu. [1]: #xref-corresp-1-1